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TransRUL: A Transformer-Based Multihead Attention Model for Enhanced Prediction of Battery Remaining Useful Life

Author

Listed:
  • Umar Saleem

    (School of Automation, Northwestern Polytechnical University, Xi’an 710072, China)

  • Wenjie Liu

    (School of Automation, Northwestern Polytechnical University, Xi’an 710072, China)

  • Saleem Riaz

    (School of Automation, Northwestern Polytechnical University, Xi’an 710072, China)

  • Weilin Li

    (School of Automation, Northwestern Polytechnical University, Xi’an 710072, China)

  • Ghulam Amjad Hussain

    (College of Engineering and IT, University of Dubai, Dubai 14143, United Arab Emirates)

  • Zeeshan Rashid

    (Department of Electrical Engineering, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan)

  • Zeeshan Ahmad Arfeen

    (Department of Electrical Engineering, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan)

Abstract

The efficient operation of power-electronic-based systems heavily relies on the reliability and longevity of battery-powered systems. An accurate prediction of the remaining useful life (RUL) of batteries is essential for their effective maintenance, reliability, and safety. However, traditional RUL prediction methods and deep learning-based approaches face challenges in managing battery degradation processes, such as achieving robust prediction performance, to ensure scalability and computational efficiency. There is a need to develop adaptable models that can generalize across different battery types that operate in diverse operational environments. To solve these issues, this research work proposes a TransRUL model to enhance battery RUL prediction. The proposed model incorporates advanced approaches of a time series transformer using a dual encoder with integration positional encoding and multi-head attention. This research utilized data collected by the Centre for Advanced Life Cycle Engineering (CALCE) on CS_2-type lithium-ion batteries that spanned four groups that used a sliding window technique to generate features and labels. The experimental results demonstrate that TransRUL obtained superior performance as compared with other methods in terms of the following evaluation metrics: mean absolute error (MAE), root-mean-squared error (RMSE), and R 2 values. The efficient computational power of the TransRUL model will facilitate the real-time prediction of the RUL, which is vital for power-electronic-based appliances. This research highlights the potential of the TransRUL model, which significantly enhances the accuracy of battery RUL prediction and additionally improves the management and control of battery-based systems.

Suggested Citation

  • Umar Saleem & Wenjie Liu & Saleem Riaz & Weilin Li & Ghulam Amjad Hussain & Zeeshan Rashid & Zeeshan Ahmad Arfeen, 2024. "TransRUL: A Transformer-Based Multihead Attention Model for Enhanced Prediction of Battery Remaining Useful Life," Energies, MDPI, vol. 17(16), pages 1-24, August.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:16:p:3976-:d:1454106
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    References listed on IDEAS

    as
    1. Yunlong Han & Conghui Li & Linfeng Zheng & Gang Lei & Li Li, 2023. "Remaining Useful Life Prediction of Lithium-Ion Batteries by Using a Denoising Transformer-Based Neural Network," Energies, MDPI, vol. 16(17), pages 1-16, August.
    2. Xu, Xiaodong & Tang, Shengjin & Yu, Chuanqiang & Xie, Jian & Han, Xuebing & Ouyang, Minggao, 2021. "Remaining Useful Life Prediction of Lithium-ion Batteries Based on Wiener Process Under Time-Varying Temperature Condition," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
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